Dr. Amar Dhand is a neurologist and young investigator who pursues patient-oriented clinical research on social networks structure and ischemic stroke recovery. A K23 award will allow Dr. Dhand to fulfill his long-term career goal of becoming an independent clinical investigator through training in three areas: advanced social network analysis, biostatistics, and intervention research. Dr. Dhand has recruited a multidisciplinary team of mentors and designed a detailed curriculum to accomplish this goal. This training will take place at Washington University in St. Louis, an institution with a long trak record of training clinician scientists. His mentors include Dr. Jin-Moo Lee, his primary advisor, who is a neurologist with expertise in translational stroke research, Dr. Doug Luke, a public health professor with expertise in social network analysis, and Dr. Catherine Lang, a rehabilitation investigator with expertise in longitudinal stroke outcomes research. His project begins with the understanding that social mechanisms in stroke recovery are influential and understudied. Social isolation is associated with poor recovery, while increased social support and community engagement is associated with improved recovery. Despite this knowledge, the mechanisms by which social factors influence recovery is unknown, and this has led to multiple social support interventions that have failed to improve stroke recovery or reduce caregiver burden. It is unknown whether this failure is due to an inappropriate social unit target (e.g., caregiver versus family versus friendship group), timing of intervention (e.g., immediately after stroke or delayed), or duration and potency of the program. To address this gap, he proposes a prospective cohort study of 200 stroke survivors using a novel methodology-social network analysis-to quantitatively map the social structure around a patient and its predictive value on recovery. Social network analysis is based on the theory that human behavior is most fully understood by analysis of the structure of social relations around an individual. These data may, subsequently, inform network interventions that have been efficacious in other diseases, such as addiction disorders and hypertension. We hypothesize that stroke survivors' personal social networks will become smaller and denser especially in those with more severe strokes, and certain network variables at stroke onset will independently predict functional outcomes through specific mediators. This hypothesis will be tested by the following specific aims: Aim 1 will determine changes in social network structure after stroke of varying severity; Aim 2 will assess the predictive value of social network variables at stroke onset on stroke outcomes; Aim 3 will determine the factors that mediate the relationship between social networks and stroke outcomes. This study is significant because it will show the natural history of network structures in stroke recovery, their relation to stroke outcomes, and the mediators between networks and recovery. This project is innovative because it introduces a novel analytical framework that challenges current social support models and improves the theoretical underpinnings of social support interventions.